1. Field of the Invention
This invention relates to the field of electronic article surveillance (EAS) systems, and in particular, to enhancing detection sensitivity and reliability of EAS systems operating in high noise environments.
2. Description of Related Art
In an EAS system markers or tags affixed to articles are expected to pass through an interrogation zone defined by a magnetic field generated by the EAS system. The markers or tags have a characteristic signal response which can be detected by the EAS system. There are contradictory goals in implementing EAS systems. One goal is to maximize the likelihood of detecting a marker or tag. Achieving this goal requires maximum sensitivity to the received signal. Another goal is to eliminate false detection, so as to avoid unnecessary embarrassment of a customer who is not really stealing an article. Achieving this goal requires maximum immunity to electrical background noise received from the interrogation zone, by itself or in conjunction with a valid marker response. Improving sensitivity tends to reduce noise immunity and improving noise immunity tends to reduce sensitivity.
Electrical noise is herein considered to be any undesired signal, with regard to amplitude or frequency, or both, present at the detector of an EAS receiver. Electrical noise comes from two primary sources, namely from sources internal to the EAS system and from sources external of the EAS system. Internal sources include system generated signals and circuit noise. External sources can be divided into environmental sources, such as lightning, and man-made sources, such as noise on local electrical wiring and nearby electrical devices. Noise from any source competes with desired signals and thus reduces system sensitivity.
Internal, system-generated signals can often be gated or filtered, or reduced by physical isolation, for example by shielding. Circuit noise, including thermal and junction noise can often be reduced through careful layout techniques and component selection.
External sources represent the largest source of noise faced by an EAS receiver. Environmental noise is broad-spectrum, and so contains energy within the receiver's bandwidth. However, environmental noise is also intermittent, and so can usually be treated through some form of time dependent processing. Man-made sources are the most pernicious form of interference. Electrical devices are often located close to an EAS system and therefor have high signal levels. Many such sources produce signals having an energy spectrum falling at or near the system's band of interest, and often the sources are active continuously, resulting in a constant loss in sensitivity.
Due to the bandpass filters typically used on EAS receivers, broad-spectrum noise appearing at the input to an EAS receiver, including equivalent input circuit noise, appears as normally distributed or Gaussian noise at the receiver's detector. While this Gaussian noise is random in nature, it can be processed statistically with some success. In order to provide maximum sensitivity consistent with minimum false alarms, EAS receivers typically operate with a 10-12 dB signal-plus-noise to noise ratio, conveniently designated (S+N)/N. A tag or marker signal must therefore be 3 to 4 times greater than the average background noise level to be considered significant. In low noise environments, this does not pose a problem. However, when nearby man-made sources, such as television and computer monitors, motor speed controllers, lamp dimmers, neon signs, and the like, produce a high noise environment, these high level signals result in a correspondingly high noise average, raising the detection threshold proportionately and seriously reducing sensitivity. These man-made sources are non-Gaussian in nature, having high energy levels at particular frequencies or bands of frequencies. Unfortunately, previous receiver designs have been unable to characterize and adjust for the differences between random Gaussian noise and coherent man-made sources.
The operation of a pulsed magnetic EAS system available from Sensormatic Corporation synchronizes its operation by sensing local power line zero crossings. Each line cycle is divided up into six time windows: three windows for transmission and three windows for reception. The first transmit-receive window sequential pair, designated Phase A herein, occurs at 0.degree. with respect to the zero crossing. The second transmit-receive window sequential pair, designated Phase B herein, occurs at 120.degree. with respect to the zero crossing. The third transmit-receive window sequential pair, designated Phase C herein, occurs at 240.degree. with respect to the zero crossing.
Previous implementations of this pulsed magnetic EAS system receiver computes a detection threshold based on a fixed signal-to-noise ratio (SNR), or more accurately, a signal-plus-noise-to-noise ratio as explained above. The receivers keep track of the background noise and continuously compute a running arithmetic average. The detection threshold is set to a defined number of dB above this average. The default is typically 12 dB, or four times (4.times.) the average. When a tag or marker enters the interrogation field of the system, the signal level of the tag must exceed this detection threshold, also referred to as a validation threshold, in order for processing to continue. The (S+N)/R is programmable at the time of system installation, but once set, the system uses this fixed ratio to track changes in background noise. A margin of 12 dB has been found to provide the best compromise between maximum sensitivity and false alarm immunity. Assuming Gaussian noise at the detector, the chance of the system falsely initiating a validation sequence due to noise is less than 0.2%.
The best EAS system performance is achieved when its receiver is operating at its maximum sensitivity consistent with the dynamics of the noise environment. Any factors which keep the receiver from achieving this maximum sensitivity degrade system performance.
Known pulsed magnetic EAS systems track background noise by computing a simple arithmetic moving average, in a manner according to Equation 1 below, of the instantaneous noise values sampled during receiver time windows wherein the receiver was not anticipating signals from a magnetic tag or marker, i.e. windows not preceded by a transmitter burst. The assumption is that, noise events being random, signal levels due to noise should be equal whether they are sampled during a receiver noise window or during a "tag window", that is, when a magnetic tag or marker would produce a response. Whenever the signal levels detected during a tag window exceed those during a corresponding noise window by a predetermined margin, the assumption is that the tag window response is due to a tag or magnetic marker within the system's interrogation field, and a validation sequence is initiated to determine if the characteristics of the tag window response is consistent with a valid marker. These characteristics include, for example, whether the signal is of sufficient amplitude and duration, whether the signal has the correct frequency characteristic and whether the signal tracks or follows the transmitter burst. Any time the system erroneously initiates a validation sequence, there is an increased chance of initiating a false alarm.
For sample size n, the arithmetic average is calculated as follows: ##EQU1##
The problem with relying on a simple arithmetic average is that the system reacts twice for each change in the noise environment. The first reaction occurs when the sample enters the group being averaged. The second reaction occurs when the sample leaves the group being averaged. As long as a sample remains part of the group of samples in a moving average, the sample exerts the same influence or weighting factor on the average. When the sample drops out of the sample group, the average undergoes a second transient event which does not correspond to any real change in the noise environment. This can lead to initiation of undesired validation sequences.
Assume, for example, a receiver is operating in an environment with no magnetic markers in the system's interrogation field, and further assume a relatively constant background noise level. The system will compute a moving average of the background noise and determine a validation threshold at some defined number of dB above this average. Now assume a particular noise window wherein the instantaneous noise components with their respective phase characteristics combine so as to cancel each other. The resultant noise reading for that window could be significantly lower than previous windows. This lower than normal sample is added to the sample group to compute the moving average and it substantially lowers the average. Now the typical values, due to background noise, in the tag window can exceed the validation threshold and the system erroneously enters a validation sequence. Depending on the size of the sample group which makes up the moving average, the validation threshold could be exceeded for some time, perhaps long enough to complete a validation sequence and initiate a false alarm.